import argparse
import os
import numpy as np
import math

import torchvision.transforms as transforms
from torchvision.utils import save_image
from torch.utils.data import DataLoader
from torchvision import datasets
from torch.autograd import Variable

import torch.nn as nn
import torch.nn.functional as F
import torch
import torchvision

os.makedirs('images', exist_ok=True)

parser = argparse.ArgumentParser()
parser.add_argument('--n_epochs', type=int, default=200, help='number of epochs of training ')
parser.add_argument('--batch_size', type=int, default=64, help='size of batches ')
parser.add_argument('--lr', type=float, default=0.0002, help='adam:learning rate ')
parser.add_argument('--b1', type=float, default=0.5, help='adam decay of first order momentum')
parser.add_argument('--b2', type=float, default=0.999, help='adam decay of first order momentum')
parser.add_argument('--n_cpu', type=int, default=8, help='number of cpu threads to use during batch generation ')
parser.add_argument('--latent_dim', type=int, default=100, help='number of cpu threads to use during training ')
parser.add_argument('--sample_interval', type=int, default=400, help='interval between image sampling ')
parser.add_argument('-img_size', type=int, default=32, help='size of each image dimension')
parser.add_argument('--channels', type=int, default=1, help='number of image channels')
opt = parser.parse_args()
print(opt)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')


def weights_init_normal(m: torch.nn.Module):
    classname: str = m.__class__.__name__
    if classname.find('Conv') != -1:
        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find("BatchNorm2d") != -1:
        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)
        torch.nn.init.constant_(m.bias.data, 0.0)


class Generator(nn.Module):
    def __init__(self):
        super(Generator, self).__init__()
        self.init_size = opt.img_size // 4
        self.l1 = nn.Sequential(nn.Linear(opt.latent_dim, 128 * self.init_size ** 2))
        self.conv_blocks = nn.Sequential(
            nn.BatchNorm2d(128)
            , nn.Upsample(scale_factor=2)
            , nn.Conv2d(128, 128, 3, stride=1, padding=1)
            , nn.BatchNorm2d(128, 0.8)
            , nn.LeakyReLU(0.2, inplace=True)
            , nn.Upsample(scale_factor=2)
            , nn.Conv2d(128, 64, 3, stride=1, padding=1)
            , nn.BatchNorm2d(64, 0.8)
            , nn.LeakyReLU(0.2, inplace=True)
            , nn.Conv2d(64, opt.channels, kernel_size=3, stride=1, padding=1)
            , nn.Tanh()
        )

    def forward(self, z):
        out = self.l1(z)
        out = out.view(out.shape[0], 128, self.init_size, self.init_size)
        img = self.conv_blocks(out)
        return img


class Discriminator(nn.Module):
    def __init__(self):
        super(Discriminator, self).__init__()

        def discriminator_block(in_filters, out_filters, bn=True):
            block = [nn.Conv2d(in_filters, out_filters, kernel_size=3, stride=2, padding=1)
                , nn.LeakyReLU(0.2, inplace=True)
                , nn.Dropout2d(0.25)]
            if bn:
                block.append(nn.BatchNorm2d(out_filters, 0.8))
            return block

        self.model = nn.Sequential(
            *discriminator_block(in_filters=opt.channels, out_filters=16, bn=False)
            , *discriminator_block(16, 32)
            , *discriminator_block(32, 64)
            , *discriminator_block(64, 128)
        )
        # The height and width of downsampled image
        ds_size = opt.img_size // 2 ** 4
        self.adv_layer = nn.Sequential(nn.Linear(128 * ds_size ** 2, 1), nn.Sigmoid())

    def forward(self, img):
        out = self.model(img)
        out = out.view(out.shape[0], -1)
        validity = self.adv_layer(out)
        return validity


# loss function
adversarial_loss = torch.nn.BCELoss()

# Initialize generator and discriminator
generator = Generator().to(device=device)
discriminator = Discriminator().to(device=device)

# Initialize weights
generator.apply(weights_init_normal)
discriminator.apply(weights_init_normal)

# Configure data loader
os.makedirs("../../data/mnist", exist_ok=True)
datasets = datasets.MNIST('../../data/mnist', train=True, transform=transforms.Compose(
    [
        transforms.Resize(opt.img_size)
        , transforms.ToTensor()
        , transforms.Normalize([0.5], [0.5])
    ]
))
dataloader = torch.utils.data.DataLoader(dataset=datasets
                                         , batch_size=opt.batch_size
                                         , num_workers=opt.n_cpu
                                         , shuffle=True)
# Optimizers
optimizer_G = torch.optim.Adam(generator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
optimizer_D = torch.optim.Adam(discriminator.parameters(), lr=opt.lr, betas=(opt.b1, opt.b2))
Tensor = torch.cuda.FloatTensor if torch.cuda.is_available() else torch.FloatTensor

# Training
for epoch in range(opt.n_epochs):
    for i, (imgs, _) in enumerate(dataloader):
        # Adversarial ground truths
        valid = Variable(Tensor(imgs.shape[0], 1).fill_(1.0), requires_grad=False)  # 建立标签
        fake = Variable(Tensor(imgs.shape[0], 1).fill_(0.0), requires_grad=False)

        # Configure input 
        real_imgs = Variable(imgs.type(Tensor))

        ################### Train Generator ###########################
        optimizer_G.zero_grad()
        z = Variable(Tensor(np.random.normal(0, 1, (imgs.shape[0], opt.latent_dim))))  # sample noise

        gen_imgs = generator(z)  # Generate a batch of noise

        g_loss = adversarial_loss(discriminator(gen_imgs), valid)
        g_loss.backward()
        optimizer_G.step()

        ################### Train Discriminator ######################
        optimizer_D.zero_grad()

        real_loss = adversarial_loss(discriminator(real_imgs), valid)
        fake_loss = adversarial_loss(discriminator(gen_imgs.detach()), fake)  # 从生成的图片这里剥掉图节点，从而不会反向传播到生成器中
        d_loss = (real_loss + fake_loss) / 2
        d_loss.backward()
        optimizer_D.step()
        print(
            '[Epoch {}/{} [Batch {}/{} [D loss: {}][G loss: {}'.format(epoch + 1, opt.n_epochs, i, len(dataloader),
                                                                       d_loss.item(), g_loss.item())
        )
        batches_done = epoch * len(dataloader) + i
        if batches_done % opt.sample_interval == 0:
            save_image(gen_imgs.data[:25], 'images/%d.png' % batches_done, nrow=5, normalize=True)
